TY - JOUR
T1 - Automatic identification of functional clusters in fMRI data using spatial dependence
AU - Ma, Sai
AU - Correa, Nicolle M.
AU - Li, Xi Lin
AU - Eichele, Tom
AU - Calhoun, Vince D.
AU - Adali, Tlay
N1 - Funding Information:
Manuscript received July 7, 2011; accepted August 9, 2011. Date of publication September 6, 2011; date of current version November 18, 2011. This work was supported in part by the National Institutes of Health under Grant R01 EB000840, Grant R01 EB005846, and Grant 5P20_RR021938. The work of T. Eichele was supported by the Norwegian Research Council under Grant BILATGRUNN. Asterisk indicates corresponding author. *S. Ma is with the Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD 21250 USA (e-mail: saima1@umbc.edu).
PY - 2011/12
Y1 - 2011/12
N2 - In independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data, extracting a large number of maximally independent components provides a detailed functional segmentation of brain. However, such high-order segmentation does not establish the relationships among different brain networks, and also studying and classifying components can be challenging. In this study, we present a multidimensional ICA (MICA) scheme to achieve automatic component clustering. In our MICA framework, stable components are hierarchically grouped into clusters based on higher order statistical dependencemutual informationamong spatial components, instead of the typically used temporal correlation among time courses. The final cluster membership is determined using a statistical hypothesis testing method. Since ICA decomposition takes into account the modulation of the spatial maps, i.e., temporal information, our ICA-based approach incorporates both spatial and temporal information effectively. Our experimental results from both simulated and real fMRI datasets show that the use of spatial dependence leads to physiologically meaningful connectivity structure of brain networks, which is consistently identified across various ICA model orders and algorithms. In addition, we observe that components related to artifacts, including cerebrospinal fluid, arteries, and large draining veins, are grouped together and encouragingly distinguished from other components of interest.
AB - In independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data, extracting a large number of maximally independent components provides a detailed functional segmentation of brain. However, such high-order segmentation does not establish the relationships among different brain networks, and also studying and classifying components can be challenging. In this study, we present a multidimensional ICA (MICA) scheme to achieve automatic component clustering. In our MICA framework, stable components are hierarchically grouped into clusters based on higher order statistical dependencemutual informationamong spatial components, instead of the typically used temporal correlation among time courses. The final cluster membership is determined using a statistical hypothesis testing method. Since ICA decomposition takes into account the modulation of the spatial maps, i.e., temporal information, our ICA-based approach incorporates both spatial and temporal information effectively. Our experimental results from both simulated and real fMRI datasets show that the use of spatial dependence leads to physiologically meaningful connectivity structure of brain networks, which is consistently identified across various ICA model orders and algorithms. In addition, we observe that components related to artifacts, including cerebrospinal fluid, arteries, and large draining veins, are grouped together and encouragingly distinguished from other components of interest.
KW - Functional magnetic resonance imaging (fMRI)
KW - independent component analysis (ICA)
KW - multidimensional independent component analysis (MICA)
KW - spatial dependence
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U2 - 10.1109/TBME.2011.2167149
DO - 10.1109/TBME.2011.2167149
M3 - Article
C2 - 21900068
AN - SCOPUS:82155191306
VL - 58
SP - 3406
EP - 3417
JO - IRE transactions on medical electronics
JF - IRE transactions on medical electronics
SN - 0018-9294
IS - 12 PART 1
M1 - 6009176
ER -